{"id":"https://openalex.org/W7117879371","doi":"https://doi.org/10.1109/milcom64451.2025.11310378","title":"SemanticRF: Towards Robust RF Scene Retrieval Using Natural Language Supervision","display_name":"SemanticRF: Towards Robust RF Scene Retrieval Using Natural Language Supervision","publication_year":2025,"publication_date":"2025-10-06","ids":{"openalex":"https://openalex.org/W7117879371","doi":"https://doi.org/10.1109/milcom64451.2025.11310378"},"language":null,"primary_location":{"id":"doi:10.1109/milcom64451.2025.11310378","is_oa":false,"landing_page_url":"https://doi.org/10.1109/milcom64451.2025.11310378","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"MILCOM 2025 - 2025 IEEE Military Communications Conference (MILCOM)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5115027237","display_name":"Trey Crump","orcid":null},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Trey Crump","raw_affiliation_strings":["Yale University,New Haven,CT,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University,New Haven,CT,USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072222232","display_name":"Ali Maatouk","orcid":"https://orcid.org/0000-0002-3436-7068"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ali Maatouk","raw_affiliation_strings":["Yale University,New Haven,CT,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University,New Haven,CT,USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075960743","display_name":"Kemal Davasl\u0131o\u011flu","orcid":"https://orcid.org/0000-0003-2745-6764"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kemal Davaslioglu","raw_affiliation_strings":["Nexcepta Inc.,Gaithersburg,MD,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nexcepta Inc.,Gaithersburg,MD,USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007158362","display_name":"Sastry Kompella","orcid":"https://orcid.org/0000-0001-7696-6213"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sastry Kompella","raw_affiliation_strings":["Nexcepta Inc.,Gaithersburg,MD,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nexcepta Inc.,Gaithersburg,MD,USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014892027","display_name":"Leandros Tassiulas","orcid":"https://orcid.org/0000-0003-0932-774X"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Leandros Tassiulas","raw_affiliation_strings":["Yale University,New Haven,CT,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University,New Haven,CT,USA","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.61399787,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1347","last_page":"1352"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9696999788284302,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9696999788284302,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.011599999852478504,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.0017999999690800905,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6241999864578247},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.5162000060081482},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.460999995470047},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.45660001039505005},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.398499995470047},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.398499995470047}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7524999976158142},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6241999864578247},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6007999777793884},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.5162000060081482},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.460999995470047},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.45660001039505005},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44920000433921814},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.398499995470047},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.398499995470047},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3553999960422516},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.33009999990463257},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.3131999969482422},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.30300000309944153},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.29670000076293945},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2581000030040741}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/milcom64451.2025.11310378","is_oa":false,"landing_page_url":"https://doi.org/10.1109/milcom64451.2025.11310378","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"MILCOM 2025 - 2025 IEEE Military Communications Conference (MILCOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6027998924255371,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"},{"id":"https://openalex.org/F4320338294","display_name":"Air Force Research Laboratory","ror":"https://ror.org/02e2egq70"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2272847350","https://openalex.org/W2402669159","https://openalex.org/W2773170971","https://openalex.org/W2996613410","https://openalex.org/W3036812998","https://openalex.org/W4224086644"],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,83,163],"AI":[3],"and":[4,49,94,132],"machine":[5],"learning":[6,114],"have":[7,42,56],"been":[8],"driven":[9],"by":[10],"the":[11,164],"emergence":[12],"of":[13,20,25],"foundation":[14,104,161],"models,":[15,33,134],"large":[16],"pre-trained":[17],"architectures":[18],"capable":[19],"solving":[21],"a":[22,103,117,123,157],"wide":[23],"range":[24],"downstream":[26],"tasks":[27,150],"with":[28],"minimal":[29],"task-specific":[30],"tuning.":[31],"These":[32],"which":[34],"learn":[35],"unified":[36,118],"representations":[37],"from":[38,61],"diverse,":[39],"large-scale":[40,124],"datasets,":[41],"transformed":[43],"domains":[44],"such":[45,73,151],"as":[46,74,152],"language,":[47],"vision,":[48],"biology.":[50],"However,":[51],"RF":[52,107,153,165],"signal":[53],"processing":[54],"systems":[55],"yet":[57],"to":[58,115,148],"fully":[59],"benefit":[60],"this":[62,98],"paradigm":[63],"shift.":[64],"Traditional":[65],"approaches":[66],"remain":[67],"focused":[68],"on":[69,122,139],"closed-set,":[70],"single-task":[71],"objectives":[72],"automatic":[75],"modulation":[76,129],"classification":[77],"or":[78],"emitter":[79],"identification,":[80],"limiting":[81],"adaptability":[82],"dynamic":[84],"scenarios":[85],"that":[86],"demand":[87],"flexible,":[88],"general-purpose":[89],"reasoning":[90],"across":[91],"unseen":[92],"waveforms":[93],"channel":[95,133],"conditions.":[96],"In":[97],"work,":[99],"we":[100],"introduce":[101],"SemanticRF,":[102],"model":[105],"for":[106],"scene":[108,154],"analysis":[109],"trained":[110],"via":[111],"multimodal":[112],"contrastive":[113],"produce":[116],"signal-text":[119],"embedding.":[120],"Pretrained":[121],"synthetic":[125],"dataset":[126],"spanning":[127],"diverse":[128],"types,":[130],"SNRs,":[131],"SemanticRF":[135],"delivers":[136],"strong":[137,146],"performance":[138],"traditional":[140],"RFML":[141],"benchmarks":[142],"but":[143],"also":[144],"demonstrates":[145],"generalization":[147],"novel":[149],"tagging,":[155],"highlighting":[156],"step":[158],"towards":[159],"adaptable":[160],"models":[162],"domain.":[166]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-01-01T00:00:00"}